Interstitial lung disease classification using improved DenseNet

Interstitial Lung Disease (ILD) is one of the popular respiratory diseases. The correct diagnosis of ILD is beneficial to improve the effect of treatment for patients. This paper presents an improved DenseNet called small kernel DenseNet (SK-DenseNet) to improve ILD classification performance. According to the characteristics of HRCT features of lung disease, the SK-DenseNet network is more effective to extract high level and small pathological features for ILD classification. Our experiment results show that the proposed SK-DenseNet obtains an outstanding performance (98.4%),which improves 5% performance compared with DenseNet. A comparative analysis with other CNNs, such as AlexNet, VGGNet, ResNet has also demonstrated that the effectiveness of SK-DenseNet in terms of classifying lung disease patterns is superior than those compared ones. The research has validated that using small convolution kernel is useful to improve the recognition efficiency when feature patterns are small.